Breathing Disorders
Sleep-disordered breathing (SDB) encompasses a group of disorders where the breathing pattern or quality of ventilation is abnormal during sleep. Obstructive sleep apnea (OSA), the most common such disorder (effecting possible 4-5% of the adult population), is characterized by repetitive closing or collapse of the upper airway and partial or complete diminution of breathing. The obstruction is normally ended by the patient arousing briefly when the muscles of the upper airway act to clear the obstruction. During the repetitive cycle of obstruction and arousal, the OSA patient will always continue to make “efforts” to breath; in other words there is no central or brain-mediated disruption to the breathing cycle.
Conversely, in central sleep apnea (CSA), there is a disruption to breathing which is brain or control-centre in origin. Cheyne-Stokes breathing (CSB) is one of the more common forms of CSA. It is caused by an abnormal limit-cycle instability of the patient's respiratory controller in which there are rhythmic alternating periods of waxing and waning ventilation. Patients with cardiac failure (a condition where the heart fails to pump adequately) often have CSA, especially as the condition deteriorates or where therapy has ceased to allow effective compensation by the heart. Cheyne-Stokes breathing appears as a cyclical variation in tidal volume seen in heart failure patients. The cycle consists of an apnoea or hypopnoea followed by an overshooting hyperpnoea which often (but not always) has a characteristic hump backed morphology s a.k.a. a “Sydney Harbor Bridge” shape. The exact cause of CS breathing is not fully understood. However, the characteristic waxing and waning cycle is strongly reminiscent of limit cycles in a poorly adjusted control system with a maladjusted gain or destabilizing feedback-loop delay.
Sleep-disordered breathing is undesirable in all its forms because it disrupts sleep architecture (the pattern and proportion of the different forms of sleep) leading to daytime somnolence. The repetitive cessation or diminution of ventilation causes (sometimes dramatic) drops in blood oxygenation levels. These and other complications are probably responsible for the now established sequelae of cardiovascular conditions.
The treatment of choice for OSA is continuous positive airway pressure (CPAP) as first described by Sullivan [Sullivan C E, et al. Reversal of obstructive sleep apnea by continuous positive airway pressure applied through the nares. Lancet 1981 Apr. 18; 1(8225):862-5]. CPAP is also used to treat some heart-failure patients with CSA and congestive heart failure (fluid on the lungs). However, Cheyne-Stokes breathing is ineffectively treated by CPAP and may require the application of servo-ventilation [Teschler H et al. Adaptive pressure support servo-ventilation: a novel treatment for Cheyne-Stokes respiration in heart failure. Am J Respir Crit Care Med. 2001 Aug. 15; 164(4):614-9. Berthon-Jones Ventilatory assistance for treatment of cardiac failure and Cheyne-Stokes breathing. U.S. Pat. No. 6,532,959].
Diagnosis From Multiple Signals
The gold standard for the diagnosis of SDB and sleep apnea is the polysomnograph (PSG): the measurement and recording of a multitude of physiological signals during a stay overnight in a sleep laboratory. Briefly, the PSG signal ensemble normally includes one or more signals indicative of a respiratory parameters such as patient airflow rate (for the calculation of ventilation and the detection of apneas and hypopnoeas), multiple electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) signals (for the determination of patient sleep state, position and the detection of arousals from sleep), breathing effort signals (either chest and abdominal distension bands or an esophageal pressure-measuring catheter), snore amplitude, and oxygen saturation. Another method of diagnosing SDB is polygraphy (PG) whereby a reduced number of parameters are recorded while the patient sleeps. These parameters include: nasal/oral airflow rate, snore amplitude, oxygen saturation, respiratory effort (thoracic and abdominal) and body position.
In both the PSG and the PG a breathing-effort signal is recorded to enable the discrimination of OSA events from CSA or Cheyne-Stokes breathing. (A third type of event is also possible—the mixed apnea—where the event is initiated by a centrally-mediated lack of breathing drive and ends with an airway obstruction and subsequent arousal). It is impossible for the inexperienced observer to reliably determine the type of apnea without reference to at least the flow signal and a measure of breathing effort. However, an experienced and trained observer (expert) can often readily detect patterns in a run of events (apneas/hypopnoeas) allowing a reliable determination of the type of underlying disease. This is especially true of Cheyne-Stokes breathing which has a very characteristic waxing and waning pattern of ventilation.
Simple Recording Devices
The performance of either a PSG or PG requires trained technicians, is expensive, is time consuming and can itself introduce sleep disturbances. Also, it is well known that a shortage of sleep laboratories is hampering the diagnosis and treatment of current SDB patients, let alone what is considered a vast undiagnosed population. For these reasons a type of “screening” device (e.g., the microMesan® from MAP of Germany, or the ApneaLink™ from ResMed) is available to test patients suspected of having sleep-disordered breathing. Such devices are small, recording just one or two physiological signals, and can be readily sent home with the patient for a screening study. For example: patients' nasal airflow can be recorded and later examined by a physician using a personal computer and a connection to the device. A software package would then be available to read the data from the device, show statistics and make recommendations regarding suspected sleep-related pathology.
Diagnosis Classifier
The calculation of the apnea-hypopnoea index (AHI, number of such events per hour on average) is a measure regularly used to guide the direction of either treatment or further investigation with a full PSG or PG. A computer program or algorithm which further enables the discrimination between different underlying disease states based on the recorded breathing patterns provides added guidance to the clinical pathway. A strong indication of Cheyne-Stokes disease, for example, would suggest completely different follow-up compared to the more common forms of sleep apnea.
The concept of a classifier is common to many fields where it is desirable to assign an object or an underlying state of an object to one of a number of classes. This concept is used, for example, in the fields of voice recognition (where sound bytes are classified as different words or syllables), radar detection (where visual signals are classified as enemy/friendly targets) and medical diagnosis (where test results are used to classify a patient's disease state). The design of a classifier falls under the field of Pattern Recognition and a classifier can be of the supervised type (the classifier is built from training data which has been pre-classed by a supervisor or “expert”) or unsupervised type (where the natural ordering or clustering of the data determines the different classes). Time signal classification usually relies on representing the signal at particular time points with “features”. Features are simply numbers that distill the essence of the signal at a point in time, a form of compression. A set (or vector) of features is called a “pattern”. A classifier takes a pattern and manipulates it mathematically with a suitable algorithm to produce a probability value for each of a number of classes. The pattern is assigned to the class with the highest probability.
In U.S. Pat. No. 6,839,581 there is disclosed a method for detecting CS respiration in patients with congestive heart failure by performing spectral analysis of overnight oximeter recordings to obtain a set of parameters that can be used in the construction of a classification tree and a trained neural network.
In summary, sleep-disordered breathing is a common syndrome with different underlying disease types requiring very different treatment options. There is a need for a small and relatively inexpensive screening devices that can help unblock the treatment bottleneck that currently exists at the sleep laboratory. An algorithm and diagnostic apparatus that can replicate the expert's ability to detect breathing patterns associated with particular disease states will enhance the diagnosis and treatment of patients being screened for sleep-disordered breathing, or for monitoring patients already undergoing therapy. What is needed is an algorithm for flow data in the form of classifier.
What is particularly desirable is a method and apparatus for diagnosing Cheyne-Stokes breathing from flow readings or oximeter readings by use of appropriate software in conjunction with a small hand-held device for use in a home setting.
Diagnosis of Cheyne-Stokes Respiration (“CSR”)
The diagnosis of CSR usually involves conducting a sleep study and analyzing the resulting polysomnography (“PSG”) data. In a full diagnostic PSG study, a range of biological parameters are monitored that typically include a nasal flow signal, measures of respiratory effort, pulse oximetry, sleeping position, and may include: electroencephalography (“EEG”), electrocardiography (“ECG”), electromyography (“EMG”) and electro-oculography (“EOG”). Breathing characteristics are also identified from visual features, thus allowing a clinician to assess respiratory function during sleep and evaluate any presence of CSR.
During a period of Cheyne-Stokes breathing or CSR, patterns of waxing and waning tidal volume can be seen in a nasal flow signal, which is a direct measure of pulmonary functions. This unstable behavior of breathing often extends its presence into other cardio-respiratory parameters such as blood oxygen saturation levels where cyclical changes can be seen.
While the examination by a clinician is the most comprehensive method, it is a costly process and depends heavily upon clinical experience and understanding. For effective and efficient screening of patients, a classifier algorithm has been developed by the assignee of this invention that automates the scoring process by calculating the probability of a CSR occurring based on a nasal flow signal. The algorithm is disclosed in US patent application Ser. No. 11/576,210 (U.S. Patent App. Pub. No. 20080177195) filed Mar. 28, 2007, and published as WO2006066337A1 Jun. 29, 2006. The existing algorithm is a flow-based classifier where a probability of CSR is calculated given a sequence of discrete flow values. A series of pre-processing steps are performed such as linearization of flow values, filtering and the extraction of respiratory events.
The concept of a classifier is common to many fields where it is desirable to assign an object or an underlying state of an object to one of a number of classes. This concept is used, for example, in the fields of voice recognition (where sound bytes are classified as different words or syllables), radar detection (where visual signals are classified as enemy/friendly targets) and medical diagnosis (where test results are used to classify a patient's disease state). The design of a classifier falls under the field of Pattern Recognition and a classifier can be of the supervised type (the classifier is built from training data which has been pre-classed by a supervisor or “expert”) or unsupervised type (where the natural ordering or clustering of the data determines the different classes). Time signal classification usually relies on representing the signal at particular time points with “features”. Features are simply numbers that distil the essence of the signal at a point in time, a form of compression. A set (or vector) of features is called a “pattern”. A classifier takes a pattern and manipulates it mathematically with a suitable algorithm to produce a probability value for each of a number of classes. The pattern is assigned to the class with the highest probability.
Home pulse oximetry has been proposed as an alternative tool for identification of CSR, but relies on visual inspection of the oximetry signal by a trained observer (Staniforth et al., 1998, Heart, 79:394-99).
A study of 104 subjects with Congestive Heart Failure (“CHF”) by Staniforth et al. (1998, Heart, 79, 394-399.) has examined the de-saturation index recorded in nocturnal oximetry compared to normal controls. The model yielded a specificity of 81% and a sensitivity of 87% for detecting CSR-CSA. However, the overall accuracy of the model was not provided. Those authors made no attempt to determine if pulse oximetry could be used to distinguish between CSR-CSA and Obstructive Sleep Apnea (‘OSA’). U.S. Pat. No. 5,575,285—Takanashi et al, describes measuring oxygen saturation in blood from scattered and transmitted light and performing Fourier transformation to obtain a power spectrum over a frequency range of 500 Hz to 20 kHz. However, that described method does not allow for distinction between patients with CSR and OSA.
U.S. Pat. No. 6,839,581 to Grant et al, PCT Application No. WO 01/076459 and U.S. Published Patent Application No. 2002/0002327 are entitled “Method for Detecting Cheyne-Stokes Respiration in Patients with Congestive Heart Failure.” They jointly propose a diagnostic method for CSR including performing overnight oximetry recordings and performing spectral analysis on the oximetry recordings. A classification tree or neural network based on parameters derived from a power spectral analysis determines the presence or absence of CSR.
U.S. Pat. No. 6,760,608 to Lynn is entitled “Oximetry System for Detecting Ventilation Instability.” This patent describes a pulse oximetry system used to generate a time series of oxygen saturation values. The occurrence of certain patterns along the time series is used to indicate ventilation instability.
U.S. Pat. No. 7,081,095 to Lynn et al is entitled “Centralized Hospital Monitoring System for Automatically Detecting Upper Airway Instability and for Preventing and Aborting Adverse Drug Reaction”. It proposes an automatic system of diagnosis of OSA in a computerized environment of a centralized hospital critical care system.
U.S. Pat. No. 7,309,314 to Grant et al is entitled “Method for Predicting Apnea-Hypopnea Index From Overnight Pulse Oximetry Readings.” This patent proposes a tool for predicting an Apopnea Hypopnea Index (“AHI”) for use in the diagnosis of OSA by recording pulse oximetry readings, and obtaining a delta index, oxygen saturation times and oximetry de-saturation events. A multivariate non-parametric analysis and bootstrap aggregation is performed.
U.S. Pat. No. 7,398,115 to Lynn is entitled “Pulse Oximetry Relational Alarm System for Early Recognition of Instability and Catastrophic Occurrences.” The system described in this patent has an alarm triggered by the early recognition of likely catastrophic occurrences by detecting decreases in O2 saturation coupled with either: a) decrease in pulse rate; or b) increase in respiration rate. The system of this patent is aimed at treating and detecting OSA.
None of these prior art systems are capable of reliably interpreting oximetric data to reliably discriminate OSAs from CSR and to develop a probabilistic value for such attempts at apnea discriminations.